In this paper, we propose a new communication scheme called intention sharing via leader-follower forest (IS-LFF) that explicitly models the observation-dependent leader-follower
relationships to constrain the flowings of intentions through directed edges. We adopt an attention module to learn the interdependencies between agents and
generate a leader-follower forest (LFF) based on Minimum Spanning Tree [1]}. LFF eliminates message deceiving and alleviates non-stationarity
in multi-agent systems. In addition, we propose a two-stage learning algorithm to train the proposed intention sharing scheme. We list the main contributions
as follows:
